A method for applying multilayer perceptrons to control of nonlinear systems

Author

Hu, Jinglu ; Hirasawa, Kotaro

Author_Institution

Dept. of Electr. & Electron. Syst. Eng., Kyushu Univ., Fukuoka, Japan

Volume

3

fYear

2002

fDate

18-22 Nov. 2002

Firstpage

1267

Abstract

This paper introduces a new method for applying multilayer perceptron (MLP) network to control of nonlinear systems. The MLP network is not used directly as a nonlinear controller, but used indirectly via an ARX-like macro-model. The ARX-like model incorporating MLP network is constructed in such a way that it has similar linear properties to a linear ARX model. The nonlinear controller is then designed in the same way as designing a linear controller based on a linear ARX model. Numerical simulations are carried to demonstrate the effectiveness of the new method.

Keywords

autoregressive processes; learning (artificial intelligence); multilayer perceptrons; neurocontrollers; nonlinear control systems; parameter estimation; ARX model; SISO systems; dual loop learning algorithm; multilayer perceptron; nonlinear control; nonlinear systems; parameter estimation; time invariant system; Control system synthesis; Control systems; Input variables; Linearity; Multilayer perceptrons; Neural networks; Nonlinear control systems; Nonlinear systems; Numerical simulation; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on

Print_ISBN

981-04-7524-1

Type

conf

DOI

10.1109/ICONIP.2002.1202824

Filename

1202824